Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning-based Anomaly Detection with Magnetic Data

Version 1 : Received: 3 December 2020 / Approved: 4 December 2020 / Online: 4 December 2020 (07:13:40 CET)

How to cite: Mitra, P.; Akhiyarov, D.; Araya-Polo, M.; Byrd, D. Machine Learning-based Anomaly Detection with Magnetic Data. Preprints 2020, 2020120092 (doi: 10.20944/preprints202012.0092.v1). Mitra, P.; Akhiyarov, D.; Araya-Polo, M.; Byrd, D. Machine Learning-based Anomaly Detection with Magnetic Data. Preprints 2020, 2020120092 (doi: 10.20944/preprints202012.0092.v1).

Abstract

Pipeline integrity is an important area of concern for the oil and gas, refining, chemical, hydrogen, carbon sequestration, and electric-power industries, due to the safety risks associated with pipeline failures. Regular monitoring, inspection, and maintenance of these facilities is therefore required for safe operation. Large standoff magnetometry (LSM) is a non-intrusive, passive magnetometer-based mea- surement technology that has shown promise in detecting defects (anomalies) in regions of elevated mechanical stresses. However, analyzing the noisy multi-sensor LSM data to clearly identify regions of anomalies is a significant challenge. This is mainly due to the high frequency of the data collection, mis-alignment between consecutive inspections and sensors, as well as the number of sensor measurements recorded. In this paper we present LSM defect identification approach based on ma- chine learning (ML). We show that this ML approach is able to successfully detect anomalous readings using a series of methods with increasing model complexity and capacity. The methods start from unsupervised learning with "point" methods and eventually increase complexity to supervised learning with sequence methods and multi-output predictions. We observe data leakage issues for some methods with randomized train/test splitting and resolve them by specific non-randomized splitting of training and validation data. We also achieve a 200x acceleration of support-vector classifier (SVC) method by porting computations from CPU to GPU leveraging the cuML RAPIDS AI library. For sequence methods, we develop a customized Convolutional Neural Network (CNN) architecture based on 1D convolutional filters to identify and characterize multiple properties of these defects. In the end, we report the scalability of the best-performing methods and compare them, for viability in field trials.

Subject Areas

anomaly detection; machine Learning; large stand off magnetometry; multimodal data; RAPIDS-AI

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